scholarly journals Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data

2018 ◽  
Vol 7 (10) ◽  
pp. 411 ◽  
Author(s):  
Gordana Kaplan ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Strong Correlation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Complex Structure ◽  
Remote Sensing Data ◽  
Acquisition Time ◽  
Landsat 8 ◽  
Optical Parameters

As wetlands are one of the world’s most important ecosystems, their vulnerability necessitates the constant monitoring and mapping of their changes. Satellite-based remote sensing has become an essential data source for mapping and monitoring wetlands. As wetlands are dynamic ecosystems, their classification depends on many different parameters. However, considering their complex structure; wetlands tend to be challenging land cover for classification, which sometimes requires the use of multi-sensor remote sensing techniques. The objectives of this study were: (i) to investigate the monthly dynamics of several wetland classes using multi-sensor parameters; (ii) to find correlations between the investigated parameters. Thus, we extracted the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8, and extracted dual polarization backscatter values (VH-VV) from the Sentinel-1 satellite at a monthly period over a year. The results showed strong correlation between the LST and the NDVI values of 0.94, and strong correlation between the microwave (VH) and both thermal and optical parameters with a 0.81 correlation coefficient, while there was weak or no correlation between the VV and the other investigated parameters. We strongly recommend that future studies clarify the Sentinel-1 backscatter values in wetland areas, by taking multiple field measurements close to the image acquisition time.

scholarly journals IDENTIFIKASI STRUKTUR GEOLOGI DAN PENGARUHNYA TERHADAP SUHU PERMUKAAN TANAH BERDASARKAN DATA LANDSAT 8 DI LAPANGAN PANASBUMI BLAWAN

2016 ◽  
Vol 13 (1) ◽  
Author(s):  
Anjar Pranggawan Azhari ◽  
Sukir Maryanto ◽  
Arief Rachmansyah
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hot Springs ◽  
Remote Sensing Data ◽  
Geological Structure ◽  
Geothermal Field ◽  
Geothermal System ◽  
Landsat 8

This paper presented used remote sensing method for identification geological structure on Blawan-Ijengeothermal field and its system. Remote sensing data, specifically Landsat 8 and DEM SRTM, provide lineaments from the 753 multispectral band and the land surface temperature (LST) from single thermal infra red band using a retrieval method. Surface emissivity was determined based on Normalized Difference Vegetation Index (NDVI) of study area. Remote sensing analysis is good approach to identification of geological structure from surface that control thermal manifestation in Blawan geothermal field. It shows Blawan fault is the main structure in geothermal field which associated with high LST and hot springs. Interpretation indicated reservoir of Blawan-Ijen geothermal system spread from Plalangan to southwest area. Abstrak Penelitian ini bertujuan untuk mengidentifikasi struktur geologi dan gambaran sistem panasbumi Blawan-Ijen dengan aplikasi penginderaan jauh. Data penginderaan jauh khususnya citra multispektral komposit 753 Landsat 8 dan DEM SRTM digunakan sebagai data untuk mendelineasi struktur patahan di permukaan. Suhu permukaan tanah diperoleh dari pengolahan citra thermal inframerah Landsat 8 dengan bantuan metode semi empiris. Emisivitas permukaan diperoleh berdasarkan klasifikasi indeks vegetasi NDVI daerah penelitian. Analisis data penginderaan jauh merupakan pendekatan yang cukup baik dalam mengidentifikasi struktur geologi yang mengontrol manifestasi panasbumi Blawan. Hasil interpretasi menunjukkan patahan Blawan adalah struktur utama di daerah geothermal Blawan yang berasosiasi dengan suhu permukaan tanah yang tinggi dan deretan mata air panas. Interpretasi mengindikasikan reservoir sistem panasbumi Blawan berada di bawah permukaan Plalangan dan menerus dari Plalangan menuju arah barat daya daerah penelitian.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

scholarly journals Assessment of the Hail Damage on Agricultural Crops from North of Baragan Plain, Romania, Using Remote Sensing Data

2021 ◽  
Author(s):  
Claudiu Valeriu Angearu ◽  
Irina Ontel ◽  
Anisoara Irimescu ◽  
Burcea Sorin
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Weather Radar ◽  
Radar Data ◽  
Landsat 8 ◽  
Water Index ◽  
Hail Damage ◽  
Sentinel 2A

Abstract Hail is one of the dangerous meteorological phenomena facing society. The present study aims to analyze the hail event from 20 July 2020, which affected the villages of Urleasca, Traian, Silistraru and Căldăruşa from the Traian commune, Baragan Plain. The analysis was performed on agricultural lands, using satellite images in the optical domain: Sentinel-2A, Landsat-8, Terra MODIS, as well as the satellite product in the radar domain: Soil Water Index (SWI), and weather radar data. Based on Sentinel-2A images, a threshold of 0.05 of the Normalized Difference Vegetation Index (NDVI) difference was established between the two moments of time analyzed (14 and 21 July), thus it was found that about 4000 ha were affected. The results show that the intensity of the hail damage was directly proportional to the Land Surface Temperature (LST) difference values in Landsat-8, from 15 and 31 July. Thus, the LST difference values higher than 12° C were in the areas where NDVI suffered a decrease of 0.4-0.5. The overlap of the hail mask extracted from NDVI with the SWI difference situation at a depth of 2 cm from 14 and 21 July confirms that the phenomenon recorded especially in the west of the analyzed area, highlighted by the large values (greater than 55 dBZ) of weather radar reflectivity as well, indicating medium–large hail size. This research also reveals that satellite data is useful for cross validation of surface-based weather reports and weather radar derived products.

scholarly journals Assessing Vegetation Decline due to Pollution from Waste Cycle Activities by a Multitemporal Remote-Sensing Approach

2021 ◽  
Author(s):  
Giuseppe Mancino ◽  
Rodolfo Console ◽  
Michele Greco ◽  
Chiara Iacovino ◽  
Maria Lucia Trivigno ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Kendall Test ◽  
Landsat 8 ◽  
Polluted Sites ◽  
Exogenous Factors ◽  
Multitemporal Remote Sensing

The work consisted in identifying possible effects from heavy metals (HMs) pollution due to waste disposal activities in three potentially polluted sites located in Basilicata (Italy), where a release of pollutants with values over the thresholds allowed by the Italian legislation was detected. The potential variations in the physiological efficiency of vegetation have been analyzed through the multitemporal processing of satellite images. In detail, Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) images were used to calculate the Normalized Difference Vegetation Index (NDVI) trend over the years. Then, the multitemporal trends were analyzed using the median of Theil-Sen, a non-parametric estimator particularly suitable for the treatment of remote sensing data, being able to minimize the outlier effects due to exogenous factors. Finally, the subsequent application of the Mann-Kendall test on the trends identified by Theil-Sen slope allowed the evaluation of trends significance and, therefore, the areas characterized by the effects of contamination on vegetation. The application of the procedure to the three survey sites led to the exclusion of the presence of significant effects of HMs contamination on the vegetation surrounding the sites during the years of waste disposal activities.

scholarly journals The Sector Development at Tanjung Sakti, Mount Dempo, Bengkulu by Using Integration of Remote Sensing Data and TOPEX Gravity Satellite

2021 ◽  
Vol 873 (1) ◽  
pp. 012015
Author(s):  
Zahrah Athirah ◽  
Muhammad Dhery Mahendra
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Land Surface ◽  
Vegetation Index ◽  
Hot Springs ◽  
Gravity Data ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Volcanic Complex ◽  
Sensing Data

Abstract Mount Dempo is the highest volcano in South Sumatra, which lies between the Bukit Barisan mountains and Gumai. The mountain located in Dempo Makmur Village, Sub-district of Pagar Alam, Lahat Regency, South Sumatra is located at an altitude of 3173 meters above sea level with coordinates of 4.03 ° S 103.13 °E. Mount Dempo’s morphology is formed by pyroclastic deposits consisting of Tuff and Sand rocks. Mount Dempo’s vegetation is dominated by Cassia sp. and Camellia sinensis for upper vegetation, while Strobilanthes hamiltoniana and Strophanthus membranifolium dominate the undergrowth. The purpose of this study is to identify geological structures to predict geothermal prospect areas by integrating remote sensing data and TOPEX Gravity Satellite Data. The remote sensing data used in this study is Landsat 8. This data is used to analyze Land Surface Temperature (LST) from a single thermal infrared band, surface emissivity based on Normalization Difference Vegetation Index (NDVI) from the study area and determine structure delineation. Gravity Satellite Data is used to map gravity anomalies in the volcanic complex of Mount Dempo. Gravity data processing produces a high anomaly zone in the northern part of the study area and is predicted as a prospect area because it is assumed to be related to the plutonic body. High density contrast indicates that there is an error in that area. In line with the error, there are several hot springs because the error serves as a pathway for geothermal fluid to rise to the surface. The study believes that with all the facts stated above, the spots which are located in Tanjung Sakti, Mount Dempo district are very prospective to be developed as a geotourism complex, in which could also increase the welfare of the local citizens.

scholarly journals Analisis Tutupan Mangrove Taman Nasional Berbak–Sembilang melalui Citra Landsat-8 dan Pemantauan LAI

2021 ◽  
Vol 24 (3) ◽  
pp. 393-401
Author(s):  
Tengku Zia Ulqodry ◽  
Andreas Eko Aprianto ◽  
Andi Agussalim ◽  
Riris Aryawati ◽  
Afan Absori
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Area Index ◽  
Sensing Data ◽  
Canopy Coverage

Berbak Sembilang National Park of South Sumatra Region (BSNP South Sumatera) is the largest mangrove ecosystem in the western part of Indonesia. Monitoring of mangrove coverage in BSNP South Sumatera carried out using Landsat-8 imagery data based on NDVI values (Normalized Difference Vegetation Index) integrated with mangrove LAI (Leaf Area Index) data. The research purpose was to analyze the mangrove coverage and mapping the density of the mangrove vegetation canopy with the integration of remote sensing data and LAI. This research conducted field survey with LAI measurement of mangrove canopy coverage and integrated with remote sensing data to validate map. The determination and correlation coefficient of NDVI and LAI value of canopy coverage was high (R2 = 0.69 ; r = 83.07).The results of research indicated that the overall distribution of the mangrove area was 94,622.05 ha. The NDVI image integration map with LAI resulted in 4 mangrove canopy density classes consisted of rare canopy (688.80 ha ; 0.73%), moderately dense canopy (1,139.55 ha ; 1.2%), dense canopy (35,003.46 ha ; 37%), and very dense canopy (57,790.20 ha ; 61.07%). Taman Nasional Berbak Sembilang wilayah Sumatera Selatan (TNBS Sumsel) merupakan kawasan ekosistem mangrove terluas di wilayah Indonesia bagian barat. Pemantauan kerapatan kanopi vegetasi mangrove di TNBS Sumsel dilakukan menggunakan data Citra Landsat-8 berdasarkan nilai NDVI (Normalized Difference Vegetation Index) yang diintegrasikan dengan data LAI (Leaf Area Index) mangrove di lapangan. Penelitian ini bertujuan untuk menganalisis tutupan vegetasi mangrove dan memetakan sebaran kerapatan kanopi mangrove dengan integrasi data penginderaan jauh dan LAI. Penelitian ini menggunakan metode pengolahan data survei lapangan dan hasil pengolahan citra satelit. Nilai koefisien determinasi dan korelasi antara nilai NDVI dengan nilai LAI tutupan Kanopi di Lapangan dikategorikan tinggi (R2 = 0,69 ; r = 83,07). Hasil penelitian menunjukkan tutupan mangrove secara keseluruhan seluas 94.622,05 ha. Peta integrasi citra NDVI dengan LAI mangrove di lapangan menghasilkan 4 kelas kerapatan kanopi mangrove yakni kanopi jarang seluas 688,80 ha (0,73%), kanopi sedang seluas 1.139,55 ha (1,2%), kanopi lebat seluas 35.003,46 ha (37%), dan kanopi sangat lebat seluas 57.790,20 ha (61,07%).

scholarly journals Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1546
Author(s):  
David Hernández-López ◽  
Laura Piedelobo ◽  
Miguel A. Moreno ◽  
Amal Chakhar ◽  
Damián Ortega-Terol ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Full Potential ◽  
Combined Use ◽  
Nested Grid ◽  
Sentinel 2

Earth Observation (EO) imagery is difficult to find and access for the intermediate user, requiring advanced skills and tools to transform it into useful information. Currently, remote sensing data is increasingly freely and openly available from different satellite platforms. However, the variety of images in terms of different types of sensors, spatial and spectral resolutions generates limitations due to the heterogeneity and complexity of the data, making it difficult to exploit the full potential of satellite imagery. Addressing this issue requires new approaches to organize, manage, and analyse remote-sensing imagery. This paper focuses on the growing trend based on satellite EO and the analysis-ready data (ARD) to integrate two public optical satellite missions: Landsat 8 (L8) and Sentinel 2 (S2). This paper proposes a new way to combine S2 and L8 imagery based on a Local Nested Grid (LNG). The LNG designed plays a key role in the development of new products within the European EO downstream sector, which must incorporate assimilation techniques and interoperability best practices, automatization, systemization, and integrated web-based services that will potentially lead to pre-operational downstream services. The approach was tested in the Duero river basin (78,859 km2) and in the groundwater Mancha Oriental (7279 km2) in the Jucar river basin, Spain. In addition, a viewer based on Geoserver was prepared for visualizing the LNG of S2 and L8, and the Normalized Difference Vegetation Index (NDVI) values in points. Thanks to the LNG presented in this paper, the processing, storage, and publication tasks are optimal for the combined use of images from two different satellite sensors when the relationship between spatial resolutions is an integer (3 in the case of L8 and S2).

scholarly journals Estimating Crop Yields at the Field Level Using Landsat and Modis Products

2018 ◽  
Vol 66 (5) ◽  
pp. 1141-1150 ◽  
Author(s):  
František Jurečka ◽  
Vojtěch Lukas ◽  
Petr Hlavinka ◽  
Daniela Semerádová ◽  
Zdeněk Žalud ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Spring Barley ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Yield Data ◽  
Field Level

Remote sensing can be used for yield estimation prior to harvest at the field level to provide helpful information for agricultural decision making. This study was undertaken in Polkovice, located at low elevations in the Czech Republic. From 2014–2016, two datasets of satellite imagery were used: the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 datasets. Satellite data were compared with yields and other observations at the level of land blocks. Winter oilseed rape, winter wheat and spring barley yield data, representing the crops planted over the analyzed period, were used for comparison. In 2016, a more detailed analysis was conducted. We tested a relationship between remote sensing data and the spatial yield variability measured by a yield monitor from a combine harvester. Correlations varied from approximately r = 0.4 to r = 0.7 with the highest correlation (r = 0.74) between yield and the Green Normalized Difference Vegetation Index collected from a drone. Vegetation indices from both Landsat 8 and the MODIS showed a positive relationship with yields for the compared period. The highest correlation was between yield and the Enhanced Vegetation Index (r = 0.8) while the lowest was between yield and the Normalized Difference Vegetation Index from MODIS (r = 0.1).

scholarly journals Detection and modeling of soil salinity variations in arid lands using remote sensing data

2021 ◽  
Vol 13 (1) ◽  
pp. 443-453
Author(s):  
Abduldaem S. Alqasemi ◽  
Majed Ibrahim ◽  
Ayad M. Fadhil Al-Quraishi ◽  
Hakim Saibi ◽  
A’kif Al-Fugara ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Prediction Models ◽  
Field Measurements ◽  
Soil Salinization ◽  
Landsat 8 ◽  
Salinity Variations

Abstract Soil salinization is a ubiquitous global problem. The literature supports the integration of remote sensing (RS) techniques and field measurements as effective methods for developing soil salinity prediction models. The objectives of this study were to (i) estimate the level of soil salinity in Abu Dhabi using spectral indices and field measurements and (ii) develop a model for detecting and mapping soil salinity variations in the study area using RS data. We integrated Landsat 8 data with the electrical conductivity measurements of soil samples taken from the study area. Statistical analysis of the integrated data showed that the normalized difference vegetation index and bare soil index showed moderate correlations among the examined indices. The relation between these two indices can contribute to the development of successful soil salinity prediction models. Results show that 31% of the soil in the study area is moderately saline and 46% of the soil is highly saline. The results support that geoinformatic techniques using RS data and technologies constitute an effective tool for detecting soil salinity by modeling and mapping the spatial distribution of saline soils. Furthermore, we observed a low correlation between soil salinity and the nighttime land surface temperature.

scholarly journals REMOTE SENSING DATA IN MUNICIPAL GOVERNMENT

2021 ◽  
Vol 30 (4) ◽  
Author(s):  
Jaroslav Nýdrle
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Information ◽  
Relevant Literature ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Sentinel 2

This article focuses on the issue of using data obtained through remote sensing methods  in the administrative district of the municipality with extended powers of Liberec (the Czech Republic). The first part of the article discusses the question of using Earth remote sensing data for city agendas in general. Then, it presents a questionnaire, created for evaluating the needs of the Liberec municipality. This questionnaire, focusing on the use of remotely sensed data, was created on the basis of a review of relevant literature. Based on the results of the questionnaire, the following spatial information requirements were chosen to be addressed: land surface temperature map - LST (Landsat 8), vegetation index - NDVI (Sentinel 2, Planet Scope), normalized difference water index - NDWI, NDWI 2 (Sentinel 2), normalized difference built-up index - NDBI (Sentinel 2). All data obtained during the creation of this study have become part of the database of the Urban Planning and GIS Department and are available to employees of the City of Liberec.

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